Spring 2023 - POL 390 D100
Data Visualization and Political Analysis (3)
Class Number: 5462
Delivery Method: In Person
Course Times + Location:
Mo 10:30 AM – 12:20 PM
HCC 1425, Vancouver
We 10:30 AM – 11:20 AM
HCC 1315, Vancouver
1 778 782-4995
Prerequisites:One of POL 201, ECON 233, STAT 203 or equivalent.
Social data and digital technologies are rapidly transforming politics and society, including election campaigns, how governments make policy, the targeting of consumers, and our interactions and connections with one another. This course offers a hands-on introduction to data science with an emphasis on data visualization for political and social analysis. Students with credit for ECON 334, ECON 387 under the title "Applied Data Analysis", or POL 339 under the title "Politics and Data Science" may not take this course for further credit. Quantitative.
Social data and digital technologies are rapidly transforming politics and society, including election campaigns, how governments make policy, the targeting of consumers, and our interactions and connections with one another. How can we use this new data and the digitization of vast public records to understand politics? This course offers a hands-on introduction to data science with an emphasis on data visualization for political and social analysis.
The objective of this course is to introduce students to data science and its use in political research with an emphasis on data visualization. Seminars focus on workflow and project management with RStudio and R Markdown, importing data, tidying data, visualizing data through exploratory analysis and interactive media, and introducing network analysis, text analysis, and machine learning. Through in-class practice, quizzes, homework, and group projects, students will learn to apply data science techniques to political research.
The seminar will take place at the Harbour Centre campus: Monday, 10:30-12:20 (HCC 1425) and Wednesday, 10:30-11:20 (HCC1315). You are required to bring a laptop to all class meetings (note: the library loans out laptop computers for up to four hours).
- Participation and Preparation for Class 10%
- Quizzes 20%
- Homework Assignments 40%
- Group project and presentation 30%
Data Visualization: A Practical Introduction by Kieran Healy. Available in hard copy or here.
R for Data Science (2017), by Hadley Wickham & Garrett Grolemund. Available in hard copy or here.
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Department Undergraduate Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html